Multiplex digital spatial profiling of proteins and RNA in fixed tissue

Merritt, Christopher R. B.; Ong, Giang T.; Church, S.; Barker, Kristi; Danaher, Patrick; Geiss, Gary; Hoang, Margaret L.; Jung, Jaemyeong; Liang, Yan; McKay-Fleisch, Jill; Nguyen, Karen; Norgaard, Zach; Sorg, Kristina; Sprague, Isaac; Warren, Charles; Warren, Sarah; Webster, Philippa; Zhou, Zhenpeng; Zollinger, Daniel R.; Dunaway, Dwayne; Mills, Gordon B.; Beechem, Joseph M.

doi:10.1038/s41587-020-0472-9

Cited by 655 publications

(644 citation statements)

References 38 publications

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“…To address this challenge, we performed digital spatial profiling (DSP) with the GeoMx platform (NanoString) and a cancer transcriptome atlas (CTA) probe set. In this method, UV-photocleavable barcode-conjugated RNA ISH probes against 1,412 target mRNAs were used to capture and profile mRNA from user-defined regions of interest (ROI) ( Figure S12A; Methods) (125). A four-color immunofluorescence slide scan for each specimen (Figure 4A) showed intratumoral diversity in tissue architecture, allowing us to profile three distinct classes of ROIs encompassing neoplastic cells with either (1) immune and CAF infiltration, (2) immune infiltration only, or (3) CAF infiltration only ( Figure 4B).…”

Section: Modulate Local Immune Micronichesmentioning

confidence: 99%

Single-nucleus and spatial transcriptomics of archival pancreatic cancer reveals multi-compartment reprogramming after neoadjuvant treatment

Hwang

Jagadeesh

Guo

et al. 2020

Preprint

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Pancreatic ductal adenocarcinoma (PDAC) remains a treatment-refractory disease. Characterizing PDAC by mRNA profiling remains particularly challenging. Previously identified bulk expression subtypes were influenced by contaminating stroma and have not yet informed clinical management, whereas single cell RNA-seq (scRNA-seq) of fresh tumors under-represented key cell types. Here, we developed a robust single-nucleus RNA-seq (snRNA-seq) technique for frozen archival PDAC specimens and used it to study both untreated tumors and those that received neoadjuvant chemotherapy and radiotherapy (CRT). Gene expression programs learned across untreated malignant cell and fibroblast profiles uncovered a clinically relevant molecular taxonomy with improved prognostic stratification compared to prior classifications. Moreover, in the increasingly-adopted neoadjuvant treatment context, there was a depletion of classical-like phenotypes in malignant cells in favor of basal-like phenotypes associated with TNF-NFkB and interferon signaling as well as the presence of novel acinar and neuroendocrine classical-like states, which may be more resilient to cytotoxic treatment. Spatially-resolved transcriptomics revealed an association between malignant cells expressing these basal-like programs and higher immune infiltration with increased lymphocytic content, whereas those exhibiting classical-like programs were linked to sparser macrophage-predominant microniches, perhaps pointing to susceptibility to distinct therapeutic strategies. Our refined molecular taxonomy and spatial resolution can help advance precision oncology in PDAC through informative stratification in clinical trials and insights into differential therapeutic targeting leveraging the immune system.

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Section: Modulate Local Immune Micronichesmentioning

confidence: 99%

Single-nucleus and spatial transcriptomics of archival pancreatic cancer reveals multi-compartment reprogramming after neoadjuvant treatment

Hwang

Jagadeesh

Guo

et al. 2020

Preprint

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“…Digital Spatial Profiling (DSP, Nanostring FOR RESEARCH USE ONLY. Not for use in diagnostic procedures) was performed as previously described 22 . In brief, tissue slides were stained with a multiplexed panel of protein antibodies contained a photocleavable indexing oligo, enabling subsequent readouts (Supplementary Figure 3).…”

Section: Dsp Data Generation and Analysismentioning

confidence: 99%

Spatial proteomic characterization of HER2-positive breast tumors through neoadjuvant therapy predicts response

McNamara

Caswell-Jin

Joshi

et al. 2020

Preprint

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Addition of HER2-targeted agents to neoadjuvant chemotherapy has dramatically improved pathological complete response (pCR) rates in early stage Human epidermal growth factor receptor 2 (HER2)-positive breast cancer. Still, up to 50% of patients have residual disease following treatment and biomarkers predictive of response are urgently needed. We performed spatial proteomic characterization using NanoString GeoMX Digital Spatial Profiling (DSP) of 122 samples from 57 HER2-positive breast tumors from the neoadjuvant TRIO-US B07 clinical trial (discovery cohort, n=28; validation cohort, n=29) sampled pre-treatment, after 14-21 days of HER2-targeted therapy and at surgery. In situ quantification of 40 tumor and immune proteins across multiple pancytokeratin- enriched regions per sample revealed that treatment results in decreased HER2 signaling and increased immune infiltration after several weeks of therapy. These changes were more dramatic in tumors that ultimately undergo pCR, and a classifier trained to predict pCR using on-treatment and pre-treatment DSP protein levels had a cross-validation mean Area Under the Receiver Operating Characteristics (AUROC) of 0.733 in the discovery cohort and validated in an independent cohort (AUROC = 0.725). Thus, we demonstrate robust stratification of sensitive tumors early during neoadjuvant HER2-targeted therapy using a multiplex spatial proteomic biomarker with implications for tailoring subsequent therapy.

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“…With a paired H&E and mIF/IHC dataset that encompasses the expression of hundreds of markers within the same (or serially-sectioned) tissue, we could begin to quantify the mutual information between histology and expression of any marker of interest. Notwithstanding the prospect of virtual multiplexing, virtual panCK IF alone could be of useful to spatial profiling platforms which use panCK IF to label tumor regions for localized spatial profiling of protein and RNA abundances in formalin-fixed tissues 36 .…”

Section: Discussionmentioning

confidence: 99%

SHIFT: speedy histological-to-immunofluorescent translation of a tumor signature enabled by deep learning

Burlingame

McDonnell

Schau

et al. 2020

Sci Rep

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Spatially-resolved molecular profiling by immunostaining tissue sections is a key feature in cancer diagnosis, subtyping, and treatment, where it complements routine histopathological evaluation by clarifying tumor phenotypes. In this work, we present a deep learning-based method called speedy histological-to-immunofluorescent translation (SHIFT) which takes histologic images of hematoxylin and eosin (H&E)-stained tissue as input, then in near-real time returns inferred virtual immunofluorescence (IF) images that estimate the underlying distribution of the tumor cell marker pan-cytokeratin (panCK). To build a dataset suitable for learning this task, we developed a serial staining protocol which allows IF and H&E images from the same tissue to be spatially registered. We show that deep learning-extracted morphological feature representations of histological images can guide representative sample selection, which improved SHIFT generalizability in a small but heterogenous set of human pancreatic cancer samples. With validation in larger cohorts, SHIFT could serve as an efficient preliminary, auxiliary, or substitute for panCK IF by delivering virtual panCK IF images for a fraction of the cost and in a fraction of the time required by traditional IF.

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Multiplex digital spatial profiling of proteins and RNA in fixed tissue

Cited by 655 publications

References 38 publications

Single-nucleus and spatial transcriptomics of archival pancreatic cancer reveals multi-compartment reprogramming after neoadjuvant treatment

Single-nucleus and spatial transcriptomics of archival pancreatic cancer reveals multi-compartment reprogramming after neoadjuvant treatment

Spatial proteomic characterization of HER2-positive breast tumors through neoadjuvant therapy predicts response

SHIFT: speedy histological-to-immunofluorescent translation of a tumor signature enabled by deep learning

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